Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/6527
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dc.contributor.authorProf. LI Yi Man, Ritaen_US
dc.contributor.authorLeung, Tat Hoen_US
dc.date.accessioned2021-03-07T09:11:59Z-
dc.date.available2021-03-07T09:11:59Z-
dc.date.issued2019-
dc.identifier.citationIn Yang, X.S., Sherratt, S., Dey, N. & Joshi, A. (eds.) (2019). Fourth international congress on information and communication technology (pp. 17-22).en_US
dc.identifier.isbn9789813293427-
dc.identifier.isbn9789813293434-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/6527-
dc.description.abstractConstruction sites are among the most hazardous venues. While most of the previous research has shed light on the human aspect, we propose to utilise the fast R-CNN object detection method to detect the construction hazard on sites and employ mixed reality to enable the artificial intelligence to detect the hazard. Fast region-based convolutional neural network object detection acquires expert knowledge to identify objects in the image. Unlike image classification, the complexity of object detection always implies an increase in complexity which demands solutions with regard to speed, accuracy and simplicity.en_US
dc.language.isoenen_US
dc.titleComputer vision and hybrid reality for construction safety risks: A pilot studyen_US
dc.typeConference Paperen_US
dc.relation.conference4th International Congress on Information and Communication Technologyen_US
dc.identifier.doi10.1007/978-981-32-9343-4_2-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Economics and Finance-
Appears in Collections:Economics and Finance - Publication
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